The RNA Centric Annotation System Analysis Report

About RCAS

RCAS (RNA Centric Annotation System) is an automated system that provides dynamic annotations for custom input files that contain transcriptomic target regions. Such transcriptomic target regions could be, for instance, peak regions detected by CLIP-Seq analysis that detect protein-RNA interactions, MeRIP-Seq analysis that detect RNA modifications (alias the epitranscriptome), or any collection of target regions at the level of the transcriptome.

The input required for the RCAS is a collection of transcriptomic target regions in 'BED' format. RCAS provides summary statistics and annotations according to the distribution of the genomic coordinates of the queried regions. Thus, the aim of RCAS is to provide the biological context and the means to quickly summarize, visualize and support hypothesis generation processes for high-throughput transcriptomic data.

GO term enrichment analysis

RCAS overlays the input target regions with the annotated protein-coding genes and calculates the Gene Ontology (GO) terms that may be enriched or depleted in the input target regions compared to the background list of protein-coding genes. A Classical Fisher's Exact Test is applied for each GO term and the p-values obtained for each GO term is corrected for multiple testing using both the False Discovery Rate and the Family-Wise Error Rate.

Gene Set Enrichment Analysis

Similarly to the GO term enrichment analysis, RCAS also detects sets of genes as annotated in the Molecular Signatures Database that are enriched or depleted in the queried target regions. Results are corrected for multiple-testing according to both the False Discovery Rate and the Family-Wise Error Rate.

1 Summary Figures

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1.1 Distribution of query regions across gene features

1.2 Distribution of query regions across gene features

1.3 Distribution of query regions across RNA genes

1.4 Distribution of query regions in the genome grouped by gene types

1.5 Distribution of query regions across the chromosomes grouped by gene features

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1.6 Interactive table of genes that overlap query regions

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2 Coverage Profiles

2.1 Coverage profile of query regions across the length of transcripts

2.2 Coverage profile of query regions across the length of Exons

2.3 Coverage profile of query regions across the 100 bp region centered on exon-intron junctions

2.4 Coverage profile of query regions across the length of introns

2.5 Coverage profile of query regions across the promoter regions

2.6 Coverage profile of query regions across the length of 5’ UTRs

2.7 Coverage profile of query regions across the length of 3’ UTRs

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3 GO term and Pathway Enrichment Results

3.1 GO Term Enrichment Results for Biological Processes

3.2 GO Term Enrichment Results for Molecular Functions

3.3 GO Term Enrichment Results for Cellular Compartments

3.4 Gene Set Enrichment Results based on MSigDB

4 TOP MEME motifs discovered in the query regions

MOTIF 1 MEME width = 8 sites = 517 llr = 2914 E-value = 4.1e-103

MOTIF 2 MEME width = 8 sites = 108 llr = 897 E-value = 7.0e-035

MOTIF 3 MEME width = 8 sites = 38 llr = 342 E-value = 1.9e+004

5 Acknowledgements

RCAS is developed by Dr. Altuna Akalin (head of the Scientific Bioinformatics Platform), Dr. Dilmurat Yusuf (Bioinformatics Scientist), and Dr. Bora Uyar (Bioinformatics Scientist) at the Berlin Institute of Medical Systems Biology (BIMSB) at the Max-Delbrueck-Center for Molecular Medicine (MDC) in Berlin.

RCAS is developed as a bioinformatics service as part of the RNA Bioinformatics Center, which is one of the eight centers of the German Network for Bioinformatics Infrastructure (de.NBI).